Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM_2.5 Predictions

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM_2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM_2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM_2.5, PM₁₀, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM_2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be −29.9W/m² at the surface, 27.0W/m² inside the atmosphere, and −2.9W/m² at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China

Evaluating urban and nonurban PM_2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset

The air quality in China has changed due to the implementation of clean air actions since 2013. Evaluating the spatial pattern of PM2.5 and the effectiveness of reducing anthropogenic emissions in urban and nonurban areas is crucial. Therefore, the China Long-term Air Pollutant dataset for PM2.5 (CLAP_PM2.5) was generated from 2010 to 2022 with a daily 0.1° resolution using the random forest model and integrating multiple data sources, including extensive in-situ PM2.5 measurements, visibility, satellite retrievals, surface and upper-level meteorological data and other ancillary data. The CLAP_PM2.5 dataset is more reliable and accurate than other public datasets. Analysis of CLAP_PM2.5 from 2010 to 2022 reveals the decrease in positive urban-nonurban PM2.5 differences and higher decreasing rates of PM2.5 in most city clusters in eastern China. Furthermore, separating meteorological and emission contributions to the PM2.5 variability by a meteorological normalization approach indicates that meteorological contribution gradually changed from unfavorable to PM2.5 reduction during 2013–2017 to favorable to decline enhancement during 2018–2022, and in urban regions, meteorological contribution is higher than that in nonurban areas. Overall, the reduction in deweathered PM2.5 concentrations highlights China's significant achievements in terms of comprehensive clean air actions.</p